基于LabVIEW的电磁超声无损检测系统的设计

2018年5月电工技术学报Vol.33 No. 10 第33卷第10期TRANSACTIONS OF CHINA ELECTROTECHNICAL SOCIETY May 2018

DOI: 10.19595/https://www.360docs.net/doc/7b5250158.html,ki.1000-6753.tces.170473

基于LabVIEW的电磁超声

无损检测系统的设计

刘素贞1,2饶诺歆1,2张闯1,2金亮3杨庆新1,2,3（1. 省部共建电工装备可靠性与智能化国家重点实验室（河北工业大学）天津 300130

2. 河北省电磁场与电器可靠性省部共建重点实验室（河北工业大学）天津 300130

3. 天津工业大学电工电能新技术天津市重点实验室天津 300387）

摘要针对无损检测技术智能化的需求，提出一种基于LabVIEW的缺陷在线识别方法。对电磁超声信号进行时域、频域及时频域结构的特征提取，并采用基于类内类间平均距离和序列前

向选择相结合的方法进行特征选择；构建基于支持向量机（SVM）的监督学习模型和多种半监督

学习模型，并分别进行缺陷识别，结果表明S4VM（S4VM）是一个相对安全的半监督支持向量

机；搭建电磁超声无损检测系统，并进行缺陷在线识别实验，实验结果验证了系统的可靠性，并

可实现电磁超声缺陷识别的图像化、数字化、智能化和系统化。

关键词：虚拟仪器特征提取支持向量机半监督学习缺陷在线识别

中图分类号：TG115.28

Design of Electromagnetic Ultrasonic Nondestructive Testing System

Based on LabVIEW

Liu Suzhen1,2 Rao Nuoxin1,2 Zhang Chuang1,2 Jin Liang3 Yang Qingxin1,2,3

（1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment

Hebei University of Technology Tianjin 300130 China

2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province

Hebei University of Technology Tianjin 300130 China

3. Key Laboratory of Advanced Electrical Engineering and Energy Technology

Tianjin Polytechnic University Tianjin 300387 China）

Abstract In the paper, a novel method based on LabVIEW is proposed for the on-line identification of crack to meet the need of intelligent nondestructive testing technology. The features of electromagnetic ultrasonic signal in time domain, frequency domain and time-frequency domain are extracted, while the feature selection is carried out combining the within-class & between-class average distance with sequential forward selection method. Based on support vector machine (SVM), the recognition model about supervised learning and semi supervised learning is constructed. The result shows that S4VM is a safer semi supervised support vector machine. The electromagnetic ultrasonic nondestructive testing system is built and the on-line defect recognition experiment is conducted. Test results show that the system is reliable, and can achieve the visualization, digitization, intellectualization and systematization of the electromagnetic ultrasonic defect recognition.

国家自然科学基金（51777052），河北省自然科学基金（E2016202260、E2017202055）和天津市自然科学基金（16JCYBJC19000）资助项目。收稿日期 2017-04-18 改稿日期 2017-05-25

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